open access

https://hdl.handle.net/2115/100491

Accurate and Fast Prediction of Intrinsically Disordered Protein by Multiple Protein Language Models and Ensemble Learning

英語

2023-10-26

American Chemical Society(ACS)

Journal of Chemical Information and Modeling

64

2901-2911

Intrinsically disordered proteins (IDPs) play a vital role in various biological processes and have attracted increasing attention over the past few decades. Predicting IDPs from primary structures of proteins offers a rapid and facile means of protein analysis without necessitating crystal structures. In particular, machine learning methods have demonstrated their potential in this field. Recently, protein language models (PLMs) are emerging as a promising approach to extracting essential information from protein sequences and have been employed in protein modeling to utilize its advantages of precision and efficiency. In this paper, we developed a novel IDP prediction method named IDP-ELM to predict the intrinsically disordered regions (IDRs) as well as their functions including disorder flexible linkers and disordered protein-binding. This method utilizes the high-dimensional representations extracted from several state-of-the-art PLMs and predicts IDRs by ensemble learning based on bidirectional recurrent neural networks. The performance of the method was evaluated on two independent test datasets from CAID (Critical assessment of protein intrinsic disorder prediction) and CAID2, indicating notable improvements in terms of area under the receiver operating characteristic (AUC), Matthew’s correlation coefficient (MCC), and F1 score. Moreover, IDP-ELM requires solely protein sequences as inputs and does not entail a time-consuming process of protein profile generation, which is a prerequisite for most existing state-of-the-art methods, enabling an accurate, fast, and convenient tool for proteome-level analysis. The corresponding reproducible source code and model weights are available at https://github.com/xu-shi-jie/idp-elm.

学術雑誌論文

Accepted Manuscript

This document is the Accepted Manuscript version of a Published Article that appeared in final form in Journal of chemical information and modeling, copyright © 2023 American Chemical Society. To access the final published article, see ACS Articles on Request.